Abstract:Detecting and locating buried objects from complex and diverse Ground Penetrating Radar(GPR) imaging is labor-intensive and time intensive. A method based on deep learning is proposed. The quantitative analysis on arbitrary targets is performed by using the Fully Convolutional One-Stage(FCOS) object detection algorithm. The target area is tracked and labeled with clustering tags. The precise location of underground target is obtained by the curve fitting. The information is reconstructed for the buried underground target. The simulation results show that this method avoids the complex calculation required by the traditional processing algorithm, and can quickly detect the target. The position and dielectric properties of the target are estimated with high precision, and the positioning error in depth is below 3 cm. Therefore, this method effectively realizes the reconstruction of the position, depth and size of the target in the underground scene.